This paper addresses the challenges of applying deep learning in active learning, particularly for high-dimensional image data. Active learning is a method where a model learns from a small amount of labeled data and selects the most informative unlabeled data points to label, reducing the overall cost and time required for training. Deep learning models, however, often require large datasets and struggle with uncertainty representation, which is crucial for effective active learning. The paper combines recent advances in Bayesian deep learning with active learning frameworks, developing a practical approach for high-dimensional data. It introduces Bayesian Convolutional Neural Networks (BCNNs) to handle image data, leveraging specialized models that can represent prediction uncertainty. The authors demonstrate their techniques on the MNIST dataset and a real-world application of skin cancer diagnosis from lesion images (ISIC2016 task). Key contributions include: 1. **Bayesian Convolutional Neural Networks (BCNNs)**: These models incorporate prior probability distributions over parameters, allowing for uncertainty representation and approximate inference using techniques like dropout. 2. **Acquisition Functions**: The paper evaluates various acquisition functions, such as Max Entropy, Variation Ratios, and BALD, and compares their performance with random acquisition. BCNNs outperform deterministic models and current active learning techniques, achieving better accuracy with fewer labeled samples. 3. **Real-World Application**: The proposed method is applied to skin cancer diagnosis, showing improved accuracy and reduced labeling costs compared to other active learning and semi-supervised learning techniques. The paper concludes by discussing future research directions, including reducing training times and improving robustness to local optima.This paper addresses the challenges of applying deep learning in active learning, particularly for high-dimensional image data. Active learning is a method where a model learns from a small amount of labeled data and selects the most informative unlabeled data points to label, reducing the overall cost and time required for training. Deep learning models, however, often require large datasets and struggle with uncertainty representation, which is crucial for effective active learning. The paper combines recent advances in Bayesian deep learning with active learning frameworks, developing a practical approach for high-dimensional data. It introduces Bayesian Convolutional Neural Networks (BCNNs) to handle image data, leveraging specialized models that can represent prediction uncertainty. The authors demonstrate their techniques on the MNIST dataset and a real-world application of skin cancer diagnosis from lesion images (ISIC2016 task). Key contributions include: 1. **Bayesian Convolutional Neural Networks (BCNNs)**: These models incorporate prior probability distributions over parameters, allowing for uncertainty representation and approximate inference using techniques like dropout. 2. **Acquisition Functions**: The paper evaluates various acquisition functions, such as Max Entropy, Variation Ratios, and BALD, and compares their performance with random acquisition. BCNNs outperform deterministic models and current active learning techniques, achieving better accuracy with fewer labeled samples. 3. **Real-World Application**: The proposed method is applied to skin cancer diagnosis, showing improved accuracy and reduced labeling costs compared to other active learning and semi-supervised learning techniques. The paper concludes by discussing future research directions, including reducing training times and improving robustness to local optima.